statistical-inferences

Evaluate Boston housing data set using statistical inferences Clone the repository and navigate to the downloaded folder.

git clone https://github.com/saurav-joshi/statistical-inferences.git

cd statistical-inferences For Linux:

conda env create -f requirements/statistical-inferences-linux.yml
source activate statistical-inferences
    source deactivate statistical-inferences

For Windows:

conda env create -f requirements/statistical-inferences-windows.yml
activate statistical-inferences
deactivate statistical-inferences

For Mac/OSX:

conda env create -f requirements/statistical-inferences-mac.yml
    source activate statistical-inferences
source deactivate statistical-inferences

Note for R Kernel installations:

R kernel installation my require an additional step. If R kernel is not installed via env file run the following command.

Activate statistical-inferences environment --- choose the command from above based on the OS
execute following command:
```
conda install -c r r-irkernel
```

Open the notebook and follow the instructions.

jupyter notebook will open the entire work space (local machine)
jupyter notebook <notebook name> will open specific notebook (local machine)

jupyter notebook --ip=0.0.0.0 --port=8080 (remote machine)

Anaconda quick tutorial:

    1. Download Anaconda from https://www.continuum.io/downloads depending on the browser. 
2. Python comes in two flavours Python3 and Python2, download the 3.X.X installer. 
3. Execute the commands mentioned above to activate and deactivate the enviornment

4. To check all the active Anaconda enviornments execute
    conda info --envs
5. To check the package version installed in an enviornment execute
    conda list <package-name>
6. To check availability of a package run
     conda search <package-name>

Name		Name	Last commit message	Last commit date
Latest commit History 130 Commits
dataset		dataset
dogImages		dogImages
images		images
pretrained-model-weights		pretrained-model-weights
requirements		requirements
CNN.ipynb		CNN.ipynb
CNN_topology.ipynb		CNN_topology.ipynb
Convolution_algo.ipynb		Convolution_algo.ipynb
GenerativeModels.pdf		GenerativeModels.pdf
Lazy Evaluation vs Upfront Evaluation... .ipynb		Lazy Evaluation vs Upfront Evaluation... .ipynb
PCA.ipynb		PCA.ipynb
README.md		README.md
RNN-Stockprice-prediction.ipynb		RNN-Stockprice-prediction.ipynb
RNN-overview.ipynb		RNN-overview.ipynb
R_data_tidying.ipynb		R_data_tidying.ipynb
R_intro.ipynb		R_intro.ipynb
R_market_basket.ipynb		R_market_basket.ipynb
R_playground_statistical_operations.....ipynb		R_playground_statistical_operations.....ipynb
R_sentiment_analysis.ipynb		R_sentiment_analysis.ipynb
R_text_analysis_natural_language_processing.ipynb		R_text_analysis_natural_language_processing.ipynb
R_unsupervised_learning_uber_data.ipynb		R_unsupervised_learning_uber_data.ipynb
arts		arts
auto_encoders.ipynb		auto_encoders.ipynb
boston_housing.ipynb		boston_housing.ipynb
churn-rate.csv		churn-rate.csv
classify.py		classify.py
embeddings.ipynb		embeddings.ipynb
first_nural_net.ipynb		first_nural_net.ipynb
generative_models.ipynb		generative_models.ipynb
grid_search_cv_20_news_group.ipynb		grid_search_cv_20_news_group.ipynb
housing.csv		housing.csv
hyper_parameters_tuning.ipynb		hyper_parameters_tuning.ipynb
linear-regression.ipynb		linear-regression.ipynb
linear_regression_python.ipynb		linear_regression_python.ipynb
logistic-regression.ipynb		logistic-regression.ipynb
logistic_regression_python.ipynb		logistic_regression_python.ipynb
mnist.ipynb		mnist.ipynb
more_supervised_algorithms.ipynb		more_supervised_algorithms.ipynb
nytimes_pull.py		nytimes_pull.py
playground_activation_functions.ipynb		playground_activation_functions.ipynb
playground_logical_operators.ipynb		playground_logical_operators.ipynb
plot_cv_boston_housing_pricing.ipynb		plot_cv_boston_housing_pricing.ipynb
plot_multi_metric_evaluation.ipynb		plot_multi_metric_evaluation.ipynb
rec.py		rec.py
sentiment_analysis.ipynb		sentiment_analysis.ipynb
sgd-intro.ipynb		sgd-intro.ipynb
sonar.ipynb		sonar.ipynb
sports		sports
statistical-inferences-mac.yml		statistical-inferences-mac.yml
stock_price_soln.ipynb		stock_price_soln.ipynb
tag_clustering.py		tag_clustering.py
tag_clustering_python.ipynb		tag_clustering_python.ipynb
tf_data_types.ipynb		tf_data_types.ipynb
tfidf.ipynb		tfidf.ipynb
timeseries_analysis.ipynb		timeseries_analysis.ipynb
transfer_learning.ipynb		transfer_learning.ipynb
transfer_learning_application.ipynb		transfer_learning_application.ipynb
vectorization.ipynb		vectorization.ipynb
visuals.py		visuals.py
wordvectors.ipynb		wordvectors.ipynb
yelp_data_challenge.ipynb		yelp_data_challenge.ipynb

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Uh oh!

Repository files navigation

statistical-inferences

About

Uh oh!

Releases

Packages

Languages

lingxinkong/statistical-inferences

Folders and files

Latest commit

History

Repository files navigation

statistical-inferences

About

Resources

Uh oh!

Stars

Watchers

Forks

Releases

Packages 0

Languages

Packages